The NCEP–FNMOC Combined Wave Ensemble Product: Expanding Benefits of Interagency Probabilistic Forecasts to the Oceanic Environment
Bibliographic record
Abstract
The U.S. National Centers for Environmental Prediction (NCEP) and the Fleet Numerical Meteorology and Oceanography Center (FNMOC) have joined forces to establish a first global multicenter ensemble system dedicated to probabilistic forecasts of windwave heights. Both centers run independent wave ensemble systems (WES), which are combined onto a multicenter system with 41 members. A performance assessment of the multicenter wave-height product is made relative to altimeter data. Computed estimates of mean errors, ability to represent uncertainty, and reliability of probabilistic forecasts indicate that the multicenter ensemble product outperforms individual WES and deterministic wave models alike. The investigation includes an evaluation made at NCEP's National Hurricane Center (NHC) of the multicenter WES product, including severe sea-state events. The interagency collaboration has provided an opportunity to investigate in more depth the properties of wave ensembles, which has led to planned improvements that are expected to increase the accuracy of probabilistic forecasts within the oceanic environment. These outcomes are expected to be of great benefit to the society, the economy, and the environment. The successful operational implementation of the multicenter product has brought new opportunities for further collaboration with operational centers in North America, and a planned upgrade to the current interagency system is the inclusion of 20 additional members from a WES under development at Environment Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".